tribe29 GmbH, supplier of the monitoring solution Checkmk, announced the availability of Checkmk version 1.6, offering enhanced monitoring capabilities for cloud and container environments as well as a new daemon for dynamic host configuration. The new Checkmk version includes new and improved features especially for monitoring cloud environments and container infrastructures. For example, Checkmk retrieves data from Amazon Web Services directly via the AWS HTTP API and monitors all major AWS services. To keep the cost for users low, the Checkmk developers have written the agent in such a way that requires as few costly API calls as possible. On top of that, they've implemented checks for monitoring AWS costs too, so that users always can get alerted about exploding costs. Azure users also find some new plug-ins that can monitor storage space, databases and virtual machines and cost of the Microsoft cloud. Checkmk uses the Azure API to communicate with the cloud. All Azure resources are automatically integrated into Checkmk, thus heavily reducing the required configuration effort. Checkmk 1.6 also contains improved checks for Docker, Kubernetes and OpenShift. The monitoring solution keeps an eye on clusters, nodes, and persistent storage as well as on pods, deployments, and micro services. Docker monitoring in particular has changed: The developers have completely revised the Docker check, enhancing and simplifying it and ensuring that it works even for older Docker versions. Checkmk 1.6 introduces the concept of labels for hosts and services. A host can have an unlimited number of labels. They work similarly to tags and can be used to create conditions for Checkmk rules. Labels are widely used in container and cloud environments, and Checkmk automatically detects and adopt them for better visibility of services and powerful configuration options. In cloud and container environments the number of hosts changes frequently because new ones are being created automatically, and old ones vanish. Since it's impractical to update the Checkmk configuration manually, the new version of the Checkmk Enterprise Edition (CEE) provides a brand-new dynamic configuration daemon (DCD). It simplifies the configuration process significantly by automatically detecting Kubernetes nodes, AWS EC2 instances, Azure resource groups, vSphere hosts and much more – thee daemon even removes hosts that no longer exist from the Checkmk monitoring. The developers have added plenty of new checks, i.e. for monitoring Elasticsearch, Splunk, SAP Hana, Oracle, Cisco UCS, Enviromux, Checkpoint, Dell, Fujitsu, and HP Management Boards. More than 100 new plug-ins have arrived in Checkmk 1.6 since version 1.5. The total number of Checkmk plug-ins has thus increased to 1700. In addition, the monitoring solution now works with i-doit, Slack, ServiceNow, JIRA, Opsgenie, VictorOps, PagerDuty and Mattermost. The new integrations ensure that Checkmk can "fill" the external platforms on its own. For example, notification rules can automatically create tickets for JIRA or ServiceNow.
The Latest
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.
The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...
The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...
Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...
If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...